The ACLIM Repository github.com/kholsman/ACLIM2 is maintained by Kirstin Holsman, Alaska Fisheries Science Center, NOAA Fisheries, Seattle WA. Multiple programs and projects have supported the production and sharing of the suite of Bering10K hindcasts and projections. Last updated: Mar 08, 2021
This repository contains R code and Rdata files for working with netcdf-format data generated from the downscaled ROMSNPZ modeling of the ROMSNPZ Bering Sea Ocean Modeling team; Drs. Hermann, Cheng, Kearney, Pilcher,Ortiz, and Aydin. The code and R resources described in this tutorial are publicly available through the ACLIM2 github repository maintained by Kirstin Holsman as part of NOAA’s ACLIM project for the Bering Sea. See Hollowed et al. 2020 for more information about the ACLIM project
We strongly recommend reviewing the following documentation before using the data in order to understand the origin of the indices and their present level of skill and validation, which varies considerably across indices and in space and time:
The Bering10K Dataset documentation: A pdf describing the dataset, including full model descriptions, inputs for specific results, and a tutorial for working directly with the ROMS native grid (Level 1 outputs).
Bering10K Simulaton Variables: A spreadsheet listing all simulations and the archived output variables associated with each, updated periodically as new simulations are run or new variables are made available.
A collection of Bering10K ROMSNPZ model documentation (including the above files) is maintained by Kelly Kearney and will be regularly updated with new documentation and publications.
The data described here are published and publicly available for use, except as explicitly noted. However, for novel uses of the data, it is strongly recommended that you consult with and consider including at least one author from the ROMSNPZ team (Drs. Hermann, Cheng, Kearney, Pilcher, Aydin, Ortiz). There are multiple spatial and temporal caveats that are best described in discussions with the authors of these data and inclusion as co-authors will facilitate appropriate application and interpretation.
TODO: add specific citations back in here
The H16 model is the original BSIERP era 10 depth layer model with a 10 Km grid. This version was used in ACLIM1.0 to dynamically downscaled 3 global scale general circulation models (GCMs) under two CMIP (Coupled Model Intercomparison Project]) phase 5 representative carbon pathways (RCP): RCP 4.5 or “moderate global carbon mitigation” and RCP 8.5 “high baseline global carbon emissions”. Details of the model and projections can be found in:
Hindcast (1979-2012; updated to 2018 during ACLIM 1.0):
Hermann, A. J., G. A. Gibson, N. A. Bond, E. N. Curchitser, K. Hedstrom, W. Cheng, M. Wang, E. D. Cokelet, P. J. Stabeno, and K. Aydin. 2016. Projected future biophysical states of the Bering Sea. Deep Sea Research Part II: Topical Studies in Oceanography 134:30–47.doi:10.1016/j.dsr2.2015.11.001
Projections of the H16 10 layer model using CMIP5 scenarios:
Hermann, A. J., G. A. Gibson, W. Cheng, I. Ortiz, K. Aydin, M. Wang, A. B. Hollowed, K. K. Holsman, and S. Sathyendranath. 2019. Projected biophysical conditions of the Bering Sea to 2100 under multiple emission scenarios. ICES Journal of Marine Science 76:1280–1304.doi:10.1093/icesjms/fsz043)
The Bering10K model was subsequently updated by Kearney et al. 2020 (30 layer and other NPZ updates) and Pilcher et al .2019 (OA and O2 dynamics) and this version is used for the projections in ACLIM2.0 under the most recent CMIP phase 6.
Hindcast (1979-2020 hindcast with OA dynamics used in ACLIM 2.0):
Kearney, K., A. Hermann, W. Cheng, I. Ortiz, and K. Aydin. 2020. A coupled pelagic-benthic-sympagic biogeochemical model for the Bering Sea: documentation and validation of the BESTNPZ model (v2019.08.23) within a high-resolution regional ocean model. Geoscientific Model Development 13:597–650.
Pilcher, D. J., D. M. Naiman, J. N. Cross, A. J. Hermann, S. A. Siedlecki, G. A. Gibson, and J. T. Mathis. 2019. Modeled Effect of Coastal Biogeochemical Processes, Climate Variability, and Ocean Acidification on Aragonite Saturation State in the Bering Sea. Frontiers in Marine Science 5:1–18.
Projections of the K20 30 layer model using CMIP6 scenarios:
Hermann et al. in prep
Cheng et al. in prep
Kearney et al. in prep
Pilcher et al. in prep (CMIP5 K20 projections) (ACLIM indices avail by permission only)
A minimal R install (for example XXX only) requires installing the ncdf4, devtools libraries (available on CRAN), and thredds R library through its github site:
install.packages(devtools)
install.packages(ncdf4)
devtools::install_github("bocinsky/thredds")
Note that each of these has multiple sub-dependent libraries and may take several minutes to install. The full install below includes installation of these packages, so you don’t need to perform this step if you perform the full install.
The full install consists of the full directory structure in the ACLIM2 Repo; this includes a substantial set of resource files including shape files and data for performing Bering Sea spatial analysis in R. This will eventually become a library package, but currently requires manual downloading of the full directory structure from github. The full install may take up to 1GB of disk space (initial download ~12MB).
If you have git installed and can work with it, this is the preferred method as it preserves all directory structure and can aid in future updating. Use this from a terminal command line, not in R, to clone the full ACLIM2 directory and subdirectories:
git clone https://github.com/kholsman/ACLIM2.git
Download the full zip archive directly from the ACLIM2 Repo using this link: https://github.com/kholsman/ACLIM2/archive/main.zip, and unzip its contents while preserving directory structure. Important: if downloading from zip, please rename the root folder from ACLIM2-main (in the zipfile) to ACLIM2 (name used in cloned copies) after unzipping, for consistency in the following examples.
This set of commands, run within R, downloads the ACLIM2 repository and unpacks it, with the ACLIM2 directory structrue being located in the specified download_path. This also performs the folder renaming mentioned in Option 2.
# Specify the download directory
main_nm <- "ACLIM2"
# Note: Edit download_path for preference
download_path <- path.expand("~/desktop")
dest_fldr <- file.path(download_path,main_nm)
url <- "https://github.com/kholsman/ACLIM2/archive/main.zip"
dest_file <- file.path(download_path,paste0(main_nm,".zip"))
download.file(url=url, destfile=dest_file)
# unzip the .zip file
setwd(download_path)
unzip (dest_file, exdir = "./",overwrite = T)
#rename the unzipped folder from ACLIM2-main to ACLIM2
file.rename(paste0(main_nm,"-main"), main_nm)
setwd(main_nm)
The remainder of this tutorial was testing in RStudio. This may work in plain R, but is untested. If you are using RStudio, open ACLIM2.Rproj in Rstudio. If using R, use setwd() to get to the main ACLIM2 directory. Then run:
# --------------------------------------
# SETUP WORKSPACE
# rm(list=ls())
tmstp <- format(Sys.time(), "%Y_%m_%d")
main <- getwd() #"~/GitHub_new/ACLIM2
source("R/make.R")
# --------------------------------------
The R/make.R command will install missing libraries (including those listed under the minimal install) and download and process multiple shapefiles for geographic analysis, it takes several minutes depending on bandwidth.
There are presently two sources of ROMSNPZ level 2 and level 3 post-processed datasets:
Level1 : (Empty; data not copied from Mox)Level2 : (full grid, rotated to lat lon from the native ROMSNPZ grid, weekly averages)
Bottom 5m : subset of variables from the bottom 5 m of the water columnBottom 5m : subset of variables for the surface 5 m of the water columnIntegrated: watercolumn integrated averages or totals for various variablesLevel3: two post-processed datasets
ACLIMsurveyrep-x.nc.: Survey replicated (variables “sampled” at the average location and date that each groundfish survey is sampled)(Note that the resampling stations need to be removed before creating bottom temperature maps)ACLIMregion-xnc.:weekly variables averaged for each survey strata (Note that area (km2) weighting should be used to combine values across multiple strata)Level1: (Empty; data not copied from Mox)Level2: (Empty; data not copied from Mox)Level3: two post-processed datasets
ACLIMsurveyrep-x.nc.: Survey replicated (variables “sampled” at the average location and date that each groundfish survey is sampled)(Note that the resampling stations need to be removed before creating bottom temperature maps)ACLIMregion-xnc.:weekly variables averaged for each survey strata (Note that area (km2) weighting should be used to combine values across multiple strata)For all files the general naming convention of the folders is: B10K-[ROMSNPZ version]_[CMIP]_[GCM]_[carbon scenario]. For example, the CMIP5 set of indices was downscaled using the H16 (Hermann et al. 2016) version of the ROMSNPZ. Three models were used to force boundary conditions( MIROC, CESM, and GFDL) under 2 carbon scenarios RCP 8.5 and RCP 4.5. So to see an individual trajectory we might look in the level3 (timeseries indices) folder under B10K-H16_CMIP5_CESM_rcp45, which would be the B10K version H16 of the CMIP5 CESM model under RCP4.5.
The public web-based ACLIM data (hindcasts & CMIP5 projections) option is available for Level3 and Level2 CMIP5 public data, it is not yet available for the embargoed CMIP6 data but through ACLIM2.0 will eventually be used to host that as well.
The ROMSNPZ team has been working with Roland Schweitzer and Peggy Sullivan to develop the ACLIM Live Access Server (LAS) to publicly host the published CMIP5 hindcasts and downscaled projections. This server is in beta testing phase and can be accessed at the following links:
Public CMIP5 and embargoed CMIP6 Level 3 netcdf (.nc) files are saved in the shared ACLIM data folder (note: Level 2 files are too large for the google drive but are available by request from Kelly Kearney.
IMPORTANT Please note that while the CMIP5 set is now public (Hermann et al. 2019; section 2.2) the CMIP6 suite is under embargo for QAQC and should not be shared outside of the ACLIM group. The ROMSNPZ team (Drs. Hermann, Cheng, Kearney, Pilcher, Adyin) are in the process of synthesizing and publishing the CMIP6 data (goal is spring 2021 for submission), following those publications the data will be made accessible to the public via the PMEL data portal, as is the case for the CMIP5 data and public hindcasts. The ROMSNPZ team has made these runs available to ACLIM2 members in order to accelerate coupling to biological and social and economic models, thus out of professional courtesy please do not publish the data without permission from all ROMSNPZ team members, it is strongly advised that some or multiple ROMSNPZ team members be included as co-authors to ensure proper application and use of the ROMSNPZ data.
For most applications you can use the ACLIM level3 post-processed indices available on the shared ACLIM drive in the root google drive data folder: 00_ACLIM_shared>02_DATA.
The Newest folder is organized by Bering10K version, General Circulation Model (GCM) and carbon scenario, e.g. B10K-H16_CMIP5_CESM_rcp45. Within each folder the following subfolders are:
Level1: (Empty; not copied from Mox)Level2: (Empty; not copied from Mox)Level3: 2 files (ACLIMsurveyrep_B10K-x.nc and ACLIMregion_B10K-x.nc )ACLIMsurveyrep_B10K-x.nc contains summer groundfish trawl “survey replicated” indices (using mean date and lat lon) (Note that the resampling stations need to be removed before creating bottom temperature maps)ACLIMregion_B10K-x.nc: contains weekly “strata” values (Note that area (km2) weighting should be used to combine values across multiple strata)There are two folders that need to be copied into the ACLIM2 folder on your computer under `~[YOURPATH]/ACLIM2/Data/in/:
00_ACLIM_shared>02_DATA>Newest. This folder contains a folder called roms_for_aclim with all the ACLIM Level3 indices for model simulations available to ACLIM members.
00_ACLIM_shared>02_DATA>Map_layers.zip. This file needs to be unzipped after you download it to your local folder. It contains (large) base maps for the code below including shp_files and geo_tif folders.
Your local ACLIM2/Data directory should look something like this when you are done downloading the data and unzipping it.
The below code will extract variables from the Level 2 and Level 3 netcdf files (.nc) and save them as compressed .Rdata files on your local Data/in/Newest/Rdata folder.
First let’s get the workspace set up, will we step through an example downloading the hindcast and a single projection (CMIP5 MIROC rcp8.5) but you can loop the code below to download the full set of CMIP5 projections.
# --------------------------------------
# SETUP WORKSPACE
# rm(list=ls())
tmstp <- format(Sys.time(), "%Y_%m_%d")
main <- getwd() #"~/GitHub_new/ACLIM2
source("R/make.R")
# --------------------------------------
Let’s take a look at the available online datasets:
# preview the datasets on the server:
url_list <- tds_list_datasets(thredds_url = ACLIM_data_url)
#display the full set of datasets:
cat(paste(url_list$dataset,"\n"))
## Constants/
## B10K-H16_CMIP5_CESM_BIO_rcp85/
## B10K-H16_CMIP5_CESM_rcp45/
## B10K-H16_CMIP5_CESM_rcp85/
## B10K-H16_CMIP5_GFDL_BIO_rcp85/
## B10K-H16_CMIP5_GFDL_rcp45/
## B10K-H16_CMIP5_GFDL_rcp85/
## B10K-H16_CMIP5_MIROC_rcp45/
## B10K-H16_CMIP5_MIROC_rcp85/
## B10K-H16_CORECFS/
## B10K-K20_CORECFS/
## files/
First we will explore the Level 2 bottom temperature data on the ACLIM Thredds server using the H16 hindcast and the H16 (CMIP5) projection for MIROC under rcp8.5. The first step is to get the data urls:
# define the simulation to download:
cmip <- "CMIP5" # Coupled Model Intercomparison Phase
GCM <- "MIROC" # Global Circulation Model
rcp <- "rcp85" # future carbon scenario
mod <- "B10K-H16" # ROMSNPZ model
hind <- "CORECFS" # Hindcast
# define the projection simulation:
proj <- paste0(mod,"_",cmip,"_",GCM,"_",rcp)
hind <- paste0(mod,"_",hind)
# get the url for the projection and hindcast datasets:
proj_url <- url_list[url_list$dataset == paste0(proj,"/"),]$path
hind_url <- url_list[url_list$dataset == paste0(hind,"/"),]$path
# preview the projection and hindcast data and data catalogs (Level 1, 2, and 3):
proj_datasets <- tds_list_datasets(thredds_url = proj_url)
hind_datasets <- tds_list_datasets(thredds_url = hind_url)
# get url for the projection and hindcast Level 2 and Level 3 catalogs
proj_l2_cat <- proj_datasets[proj_datasets$dataset == "Level 2/",]$path
proj_l3_cat <- proj_datasets[proj_datasets$dataset == "Level 3/",]$path
hind_l2_cat <- hind_datasets[hind_datasets$dataset == "Level 2/",]$path
hind_l3_cat <- hind_datasets[hind_datasets$dataset == "Level 3/",]$path
hind_l2_cat
## [1] "https://data.pmel.noaa.gov/aclim/thredds/B10K-H16_CORECFS/Level2.html"
Now that we have the URLs let’s take a look at the available Level2 datasets (currently temperature only, other variables available by request to Kelly Kearney:
Bottom 5m : bottom water temperature at 5 metersSurface 5m : surface water temperature in the first 5 metersIntegrated : Integrated water column averages for various NPZ variables # preview the projection and hindcast Level 2 datasets:
proj_l2_datasets <- tds_list_datasets(proj_l2_cat)
hind_l2_datasets <- tds_list_datasets(hind_l2_cat)
proj_l2_datasets$dataset
## [1] "Bottom 5m" "Surface 5m" "Integrated"
# get url for bottom temperature:
proj_l2_BT_url <- proj_l2_datasets[proj_l2_datasets$dataset == "Bottom 5m",]$path
hind_l2_BT_url <- hind_l2_datasets[hind_l2_datasets$dataset == "Bottom 5m",]$path
proj_l2_BT_url
## [1] "https://data.pmel.noaa.gov/aclim/thredds/B10K-H16_CMIP5_MIROC_rcp85/Level2.html?dataset=B10K-H16_CMIP5_MIROC_rcp85_Level2_bottom5m"
We can’t preview the Level 3 datasets in the same way but they are identical to those in the google drive and include two datasets
ACLIMsurveyrep_B10K-H16_CMIP5_CESM_BIO_rcp85.nc : NMFS Groundfish summer NBS and EBS survey replicated values for 60+ variablesACLIMregion_B10K-H16_CMIP5_CESM_BIO_rcp85.nc : weekly strata averages for 60+ variables weekly_vars # list of possible variables in the ACLIMregion_ files
## [1] "region_area" "Ben" "DetBen"
## [4] "Hsbl" "IceNH4" "IceNO3"
## [7] "IcePhL" "aice" "hice"
## [10] "shflux" "ssflux" "Cop_integrated"
## [13] "Cop_surface5m" "EupO_integrated" "EupO_surface5m"
## [16] "EupS_integrated" "EupS_surface5m" "Iron_bottom5m"
## [19] "Iron_integrated" "Iron_surface5m" "Jel_integrated"
## [22] "Jel_surface5m" "MZL_integrated" "MZL_surface5m"
## [25] "NCaO_integrated" "NCaO_surface5m" "NCaS_integrated"
## [28] "NCaS_surface5m" "NH4_bottom5m" "NH4_integrated"
## [31] "NH4_surface5m" "NO3_bottom5m" "NO3_integrated"
## [34] "NO3_surface5m" "PhL_integrated" "PhL_surface5m"
## [37] "PhS_integrated" "PhS_surface5m" "prod_Cop_integrated"
## [40] "prod_EupO_integrated" "prod_EupS_integrated" "prod_Eup_integrated"
## [43] "prod_Jel_integrated" "prod_MZL_integrated" "prod_NCaO_integrated"
## [46] "prod_NCaS_integrated" "prod_NCa_integrated" "prod_PhL_integrated"
## [49] "prod_PhS_integrated" "salt_surface5m" "temp_bottom5m"
## [52] "temp_integrated" "temp_surface5m" "uEast_bottom5m"
## [55] "uEast_surface5m" "vNorth_bottom5m" "vNorth_surface5m"
## [58] "fracbelow0" "fracbelow1" "fracbelow2"
Now we can download a subset of the Level2 data (full 10KM Lat Lon re-gridded data), here with an example of sampling on Aug 1 of each year:
# Currently available Level 2 variables
dl <- proj_l2_datasets$dataset # datasets
# variable list
svl <- list(
'Bottom 5m' = "temp",
'Surface 5m' = "temp",
'Integrated' = c("EupS","Cop","NCaS") )
# preview the variables, timesteps, and lat lon in each dataset:
l2_info <- scan_l2(ds_list = dl,sim_list = "B10K-H16_CORECFS" )
names(l2_info)
l2_info[["Bottom 5m"]]$vars
l2_info[["Surface 5m"]]$vars
l2_info[["Integrated"]]$vars
max(l2_info[["Integrated"]]$time_steps)
l2_info[["Integrated"]]$years
# Simulation list:
# --> --> Tinker:add additional projection scenarios here
sl <- c(hind, proj)
# Currently available Level 2 variables
dl <- proj_l2_datasets$dataset # datasets
# variables to pull from each data set
# --> --> Tinker: try subbing in other Integrated variables
# (l2_info[["Integrated"]]$vars) into the third list vector
svl <- list(
'Bottom 5m' = "temp",
'Surface 5m' = "temp",
'Integrated' = c("EupS","Cop","NCaS") )
# Let's sample the model years as close to Aug 1 as the model timesteps run:
# --> --> Tinker - try a different date
tr <- c("-08-1 12:00:00 GMT")
# grab nc files from the aclim server and convert to rdatafiles with the ID Aug1
get_l2(
ID = "_Aug1",
ds_list = dl,
trIN = tr,
sub_varlist = svl,
sim_list = sl )
Now let’s grab some of the Level 3 data and store it in the Data/in/Newest/Rdata folder. This is comparatively faster because Level 3 files are already post-processed to be in the ACLIM indices format and are relatively small:
# Simulation list:
# --> --> Tinker:add additional projection scenarios here
sl <- c(hind, proj)
# variable list
# --> --> Tinker:add additional variables to varlist
vl <- c(
"temp_bottom5m", # bottom temperature,
"NCaS_integrated", # Large Cop
"Cop_integrated", # Small Cop
"EupS_integrated") # Shelf euphausiids
# convert nc files into a long data.frame for each variable
# three options are:
# ------------------------------------
# opt 1: access nc files remotely (fast, less local storage needed)
get_l3(web_nc = TRUE, download_nc = F,
varlist = vl,sim_list = sl)
# opt 2: download nc files then access locallly:
get_l3(web_nc = TRUE, download_nc = T,
local_path = file.path(local_fl,"aclim_thredds"),
varlist = vl,sim_list = sl)
# opt 3: access existing nc files locally:
get_l3(web_nc = F, download_nc = F,
local_path = file.path(local_fl,"aclim_thredds"),
varlist = vl,sim_list = sl)
The final folder structure on your local drive in Data/in/Newest should look something like this.
Now let’s convert the CMIP6 Level3 .nc files to .Rdata files (as in section 3.2.3)
# preview the available CMIP6 data
dir(file.path(local_fl,"roms_for_aclim"))
# variable list
vl <- c(
"temp_bottom5m",
"NCaS_integrated", # Large Cop
"Cop_integrated", # Small Cop
"EupS_integrated") # Euphausiids
# define the simulation to download:
cmip <- "CMIP6" # Coupled Model Intercomparison Phase
GCM <- "MIROC" # Global Circulation Model
rcp <- "ssp585" # future carbon scenario
mod <- "B10K-K20P19" # ROMSNPZ model
hind <- "CORECFS" # Hindcast
# define the projection simulation:
proj <- paste0(mod,"_",cmip,"_",GCM,"_",rcp)
hind <- paste0("B10K-K20","_",hind)
sl <- c(hind, proj)
# opt 3: convert subset of nc files to rdata files for analysis:
get_l3(web_nc = F, download_nc = F,
local_path = file.path(local_fl,"roms_for_aclim"),
varlist = vl,sim_list = sl)
KERIM’s text and example here
Level 3 indices can be used to generate seasonal, monthly, and annual indices (like those reported in Reum et al. 2020), Holsman et al. 2020). In the section below we explore these indices in more detail using R, including using (2) above to generate weekly, monthly, and seasonal indices (e.g. Fall Zooplankton) for use in biological models. In section 3 below we explore these indices in more detail using R, including using (2) above to generate weekly, monthly, and seasonal indices (e.g. Fall Zooplankton) for use in biological models. The following examples show how to analyze and plot the ACLIM indices from the .Rdata files created in the previous step 3.
Please be sure to coordinate with ROMSNPZ modeling team members to ensure data is applied appropriately. If you need access to the raw ROMSNPZ files (netcdf, non-regridded large files located on MOX). Please contact Al Hermann or Kelly Kearney. Please note that while the CMIP5 set is now public (Hermann et al. 2019) the CMIP6 suite is under embargo for QAQC and should not be shared outside of the ACLIM group. See Section 1 above for more detail.
Once the base files and setup are loaded you can explore the index types. Recall that in each scenario folder there are two indices saved within the Level3 subfolders:
ACLIMsurveyrep_B10K-x.nc contains summer groundfish trawl “survey replicated” indices (using mean date and lat lon) (Note that the resampling stations need to be removed before creating bottom temperature maps)ACLIMregion_B10K-x.nc: contains weekly “strata” values (Note that area weighting should be used to combine values across multiple strata)First run the below set of code to set up the workspace:
# --------------------------------------
# SETUP WORKSPACE
tmstp <- format(Sys.time(), "%Y_%m_%d")
main <- getwd() #"~/GitHub_new/ACLIM2
source("R/make.R")
# --------------------------------------
# list of the scenario x GCM downscaled ACLIM indices
for(k in aclim)
cat(paste(k,"\n"))
embargoed # not yet public or published
public # published runs (CMIP5)
# get some info about a scenario:
all_info1
all_info2
# variables in each of the two files:
srvy_vars
weekly_vars
#summary tables for variables
srvy_var_def
weekly_var_def
# explore stations in the survey replicated data:
head(station_info)
Let’s start b exploring the survey replicated values for each variable. Steps 2 and 3 generated the Rdata files that are stored in the ACLIMsurveyrep_B10K-[version_CMIPx_GCM_RCP].Rdata in each corresponding simulation folder.
The code segment below will recreate the above figures.Note that if this is the first time through it may take 3-5 mins to load the spatial packages and download the files from the web (first time through only).
# if load_gis is set to FALSE in R/setup.R (default)
# we will need to load the gis layers and packages
# if this is the first time through this would be a good time
# to grab a coffee...
source("R/sub_scripts/load_maps.R")
## Loading required package: sp
##
## Attaching package: 'raster'
## The following object is masked from 'package:plotly':
##
## select
## The following object is masked from 'package:nlme':
##
## getData
## The following object is masked from 'package:tidyr':
##
## extract
## The following object is masked from 'package:dplyr':
##
## select
## The following object is masked from 'package:magrittr':
##
## extract
## Linking to GEOS 3.7.2, GDAL 2.4.2, PROJ 5.2.0
##
## Attaching package: 'maps'
## The following object is masked from 'package:purrr':
##
## map
## rgeos version: 0.5-2, (SVN revision 621)
## GEOS runtime version: 3.7.2-CAPI-1.11.2
## Linking to sp version: 1.3-1
## Polygon checking: TRUE
# first convert the station_info object into a shapefile for mapping:
station_sf <- convert2shp(station_info)
station_sf$stratum <- factor(station_sf$stratum)
# plot the stations:
p <- plot_stations_basemap(sfIN = station_sf,
fillIN = "subregion",
colorIN = "subregion") +
scale_color_viridis_d(begin = .2,end=.6) +
scale_fill_viridis_d(begin = .2,end=.6)
if(update.figs){
p
ggsave(file=file.path(main,"Figs/stations_NS.jpg"),width=5,height=5)
}
p2 <- plot_stations_basemap(sfIN = station_sf,fillIN = "stratum",colorIN = "stratum") +
scale_color_viridis_d() +
scale_fill_viridis_d()
if(update.figs){
p2
ggsave(file=file.path(main,"Figs/stations.jpg"),width=5,height=5)}
some text here
some text here
some text here
The ACLIM project utilizes the full “suite” of Bering10K model hindcasts and projections, summarized in the following table. These represent downscaled models hindcast and projections based whereby boundary conditions of the high resolution Bering10K model are forced by the coarser resolution General Circulation Models (GCM) run under Coupled Model Intercomparison Project (CMIP) phase 5 (5th IPCC Assessment Report) or phase 6 (6th IPCC Assessment Report; “AR”) global carbon mitigation scenarios. Hindcasts are similarly forced at the boundaries from global scale climate reanalysis CORE and CFS products. For full details see the Kearney 2021 Tech. Memo.
| CMIP | GCM | Scenario | Def | Years | Model | Source | Status | |
|---|---|---|---|---|---|---|---|---|
| CORECFS | Reanalysis | Hindcast | 1970 - 2018 | H16 | IEA/ACLIM | Public | ||
| CORECFS | Reanalysis | Hindcast | 1970 - 2020 | K20 | MAPP/IEA/ACLIM | Public | ||
| 5 | GFDL | RCP 4.5 | Med. mitigation | 2006 - 2099 | H16 | ACLIM/FATE | Public | |
| 5 | GFDL | RCP 8.5 | High baseline | 2006 - 2099 | H16 | ACLIM/FATE | Public | |
| 5 | GFDL | RCP 8.5bio* | High baseline | 2006 - 2099 | H16 | ACLIM/FATE | Public | |
| 5 | MIROC | RCP 4.5 | Med. mitigation | 2006 - 2099 | H16 | ACLIM/FATE | Public | |
| 5 | MIROC | RCP 8.5 | High baseline | 2006 - 2099 | H16 | ACLIM/FATE | Public | |
| 5 | CESM | RCP 4.5 | Med. mitigation | 2006 - 2099 | H16 | ACLIM/FATE | Public | |
| 5 | CESM | RCP 8.5 | High baseline | 2006 - 2080 | H16 | ACLIM/FATE | Public | |
| 5 | CESM | RCP 8.5bio* | High baseline | 2006 - 2099 | H16 | ACLIM/FATE | Public | |
| 6 | CESM | SSP585 | High baseline | 2014 - 2099 | K20P19 | ACLIM2/RTAP | Embargo | |
| 6 | CESM | SSP126 | High Mitigation | 2014 - 2099 | K20P19 | ACLIM2/RTAP | Embargo | |
| 6 | CESM | Historical | Historical | 1980 - 2014 | K20P19 | ACLIM2/RTAP | Embargo | |
| 6 | GFDL | SSP585 | High baseline | 2014 - 2099 | K20P19 | ACLIM2/RTAP | Embargo | |
| 6 | GFDL | SSP126 | High Mitigation | 2014 - 2099 | K20P19 | ACLIM2/RTAP | Embargo | |
| 6 | GFDL | Historical | Historical | 1980 - 2014 | K20P19 | ACLIM2/RTAP | Embargo | |
| 6 | MIROC | SSP585 | High baseline | 2014 - 2099 | K20P19 | ACLIM2/RTAP | Embargo | |
| 6 | MIROC | SSP126 | High Mitigation | 2014 - 2099 | K20P19 | ACLIM2/RTAP | Embargo | |
| 6 | MIROC | Historical | Historical | 1980 - 2014 | K20P19 | ACLIM2/RTAP | Embargo |
*“bio” = nutrient forcing on boundary conditions
some text here
Now let’s explore the survey replicated data in more detail and use to plot bottom temperature.
# now create plots of average BT during four time periods
time_seg <- list( '2010-2020' = c(2010:2020),
'2021-2040' = c(2021:2040),
'2041-2060' = c(2041:2060),
'2061-2080' = c(2061:2080),
'2081-2099' = c(2081:2099))
# View an individual variable (e.g., Bottom Temp)
# -------------------------------------------------------
head(srvy_vars)
head(aclim)
# assign the simulation to download
# --> --> Tinker: try selecting a different set of models to compare
sim <-"B10K-H16_CMIP5_MIROC_rcp85"
# open a "region" or strata specific nc file
fl <- file.path(sim,paste0(srvy_txt,sim,".Rdata"))
# load object 'ACLIMsurveyrep'
load(file.path(main,Rdata_path,fl))
# create local rdata files (opt 1)
if(!file.exists(fl))
get_l3(web_nc = TRUE, download_nc = F,
varlist = vl,sim_list =sim )
# Collate mean values across timeperiods and simulations
# -------------------------------------------------------
m_set <- c(9,7,8)
ms <- aclim[m_set]
# Loop over model set
for(sim in ms){
fl <- file.path(sim,paste0(srvy_txt,sim,".Rdata"))
if(!file.exists( file.path(Rdata_path,fl)) )
get_l3(web_nc = TRUE, download_nc = F,
varlist = vl,sim_list =sim )
}
# get the mean values for the time blocks from the rdata versions
# will throw "implicit NA" errors that can be ignored
mn_var_all <- get_mn_rd(modset = ms ,varUSE="temp_bottom5m")
# convert results to a shapefile
mn_var_sf <- convert2shp(mn_var_all%>%filter(!is.na(mnval)))
lab_t <- ms[2]%>%stringr::str_remove("([^-])")
p3 <- plot_stations_basemap(sfIN = mn_var_sf,
fillIN = "mnval",
colorIN = "mnval",
sizeIN=.3) +
facet_grid(simulation~time_period)+
scale_color_viridis_c()+
scale_fill_viridis_c()+
guides(
color = guide_legend(title="Bottom T (degC)"),
fill = guide_legend(title="Bottom T (degC)")) +
ggtitle(lab_t)
# This is slow but it works (repeat dev.new() twice if in Rstudio)...
dev.new()
p3
if(update.figs) ggsave(file=file.path(main,"Figs/mn_BT.jpg"),width=8,height=6)
# graphics.off()
Bottom temperature projections under differing SSP126 (top row) and SSP585 (bottom row)
The next set of indices to will explore are the weekly strata-specific values for each variable.These are stored in the ACLIMregion_B10K-[version_CMIPx_GCM_RCP].nc in each scenario folder.
# View an individual variable (e.g., Bottom Temp)
# -------------------------------------------------------
weekly_vars
aclim
sim <-"B10K-H16_CMIP5_MIROC_rcp85"
# open a "region" or strata specific nc file
fl <- file.path(sim,paste0(reg_txt,sim,".Rdata"))
var_use <- "temp_bottom5m"
vl <- c(
"temp_bottom5m",
"NCaS_integrated", # Large Cop
"Cop_integrated", # Small Cop
"EupS_integrated") # Euphausiids
# create local rdata files (opt 1)
if(!file.exists(fl))
get_l3(web_nc = TRUE, download_nc = F,
varlist = vl,sim_list = sim)
# load object 'ACLIMregion'
load(file.path(main,Rdata_path,fl))
tmp_var <- ACLIMregion%>%filter(var == var_use)
# now plot the data:
p4 <- ggplot(data = tmp_var) +
geom_line(aes(x=time,y=val,color= strata),alpha=.8)+
facet_grid(basin~.)+
ylab(tmp_var$units[1])+
ggtitle( paste(sim,tmp_var$var[1]))+
theme_minimal()
p4
if(update.figs) ggsave(file=file.path(main,"Figs/weekly_bystrata.jpg"),width=8,height=5)
# To get the average value for a set of strata, weight the val by the area:
mn_NEBS <- getAVGnSUM(strataIN = NEBS_strata, dataIN = tmp_var)
mn_NEBS$basin = "NEBS"
mn_SEBS <-getAVGnSUM(strataIN = SEBS_strata, dataIN = tmp_var)
mn_SEBS$basin = "SEBS"
p5 <- ggplot(data = rbind(mn_NEBS,mn_SEBS)) +
geom_line(aes(x=time,y=mn_val,color=basin),alpha=.8)+
geom_smooth(aes(x=time,y=mn_val,color=basin),
formula = y ~ x, se = T)+
facet_grid(basin~.)+
scale_color_viridis_d(begin=.4,end=.8)+
ylab(tmp_var$units[1])+
ggtitle( paste(sim,mn_NEBS$var[1]))+
theme_minimal()
p5
if(update.figs) ggsave(file=file.path(main,"Figs/weekly_byreg.jpg"),width=8,height=5)
Weekly indcices by sub-region
### 6.1.3. Level 3 projections: Seasonal averages
Now using a similar approach get the monthly mean values for a variable:
sim <-"B10K-H16_CMIP5_MIROC_rcp85"
# Set up seasons (this follows Holsman et al. 2020)
seasons <- data.frame(mo = 1:12,
season =factor("",
levels=c("Winter","Spring","Summer","Fall")))
seasons$season[1:3] <- "Winter"
seasons$season[4:6] <- "Spring"
seasons$season[7:9] <- "Summer"
seasons$season[10:12] <- "Fall"
vl <- c(
"temp_bottom5m",
"NCaS_integrated", # Large Cop
"Cop_integrated", # Small Cop
"EupS_integrated") # Euphausiids
# create local rdata files (opt 1)
if(!file.exists(fl))
get_l3(web_nc = TRUE, download_nc = F,
varlist = vl,sim_list = sim)
# open a "region" or strata specific file
fl <- file.path(sim,paste0(reg_txt,sim,".Rdata"))
load(file.path(main,Rdata_path,fl))
# get large zooplankton as the sum of euph and NCaS
tmp_var <- ACLIMregion%>%
filter(var%in%vl[c(2,3)])%>%
group_by(time,strata,strata_area_km2,basin)%>%
group_by(time,
strata,
strata_area_km2,
basin,
units)%>%
summarise(val =sum(val))%>%
mutate(var = "Zoop_integrated",
long_name ="Total On-shelf
large zooplankton concentration,
integrated over depth (NCa, Eup)")
rm(ACLIMregion)
head(tmp_var)
tmp_var$yr <- strptime(as.Date(tmp_var$time),
format="%Y-%m-%d")$year + 1900
tmp_var$mo <- strptime(as.Date(tmp_var$time),
format="%Y-%m-%d")$mon + 1
tmp_var$jday <- strptime(as.Date(tmp_var$time),
format="%Y-%m-%d")$yday + 1
tmp_var$season <- seasons[tmp_var$mo,2]
# To get the average value for a set of strata, weight the val by the area: (slow...)
mn_NEBS_season <- getAVGnSUM(
strataIN = NEBS_strata,
dataIN = tmp_var,
tblock=c("yr","season"))
mn_NEBS_season$basin = "NEBS"
mn_SEBS_season <- getAVGnSUM(
strataIN = SEBS_strata,
dataIN = tmp_var,
tblock=c("yr","season"))
mn_SEBS_season$basin = "SEBS"
plot_data <- rbind(mn_NEBS_season,mn_SEBS_season)
# plot Fall values:
p6 <- ggplot(data = plot_data%>%filter(season=="Fall") ) +
geom_line( aes(x = yr,y = mn_val,color=basin),alpha=.8)+
geom_smooth( aes(x = yr,y = mn_val,color=basin),
formula = y ~ x, se = T)+
facet_grid(basin~.)+
scale_color_viridis_d(begin=.4,end=.8)+
ylab(tmp_var$units[1])+
ggtitle( paste(sim,"Fall",mn_NEBS_season$var[1]))+
theme_minimal()
p6
if(update.figs)
ggsave(file=file.path(main,"Figs/Fall_large_Zoop.jpg"),width=8,height=5)
### 6.1.4. Level 3 Projections: Monthly averages Using the same approach we can get monthly averages for a given variable:
# To get the average value for a set of strata, weight the val by the area: (slow...)
mn_NEBS_season <- getAVGnSUM(
strataIN = NEBS_strata,
dataIN = tmp_var,
tblock = c("yr","mo"))
mn_NEBS_season$basin = "NEBS"
mn_SEBS_season <- getAVGnSUM(
strataIN = SEBS_strata,
dataIN = tmp_var,
tblock=c("yr","mo"))
mn_SEBS_season$basin = "SEBS"
plot_data <- rbind(mn_NEBS_season,mn_SEBS_season)
# plot Fall values:
p7 <- ggplot(data = plot_data%>%filter(mo==9) ) +
geom_line( aes(x = yr,y = mn_val,color=basin),alpha=.8)+
geom_smooth( aes(x = yr,y = mn_val,color=basin),
formula = y ~ x, se = T)+
facet_grid(basin~.)+
scale_color_viridis_d(begin=.4,end=.8)+
ylab(tmp_var$units[1])+
ggtitle( paste(aclim[2],"Sept.",mn_NEBS_season$var[1]))+
theme_minimal()
p7
if(update.figs)
ggsave(file=file.path(main,"Figs/Sept_large_Zoop.jpg"),width=8,height=5)
September large zooplankton integrated concentration
Finally we can use this approach to plot the monthly averages and look for phenological shifts:
# or average in 4 time slices by mo:
# now create plots of average BT during four time periods
time_seg <- list( '2010-2020' = c(2010:2020),
'2021-2040' = c(2021:2040),
'2041-2060' = c(2041:2060),
'2061-2080' = c(2061:2080),
'2081-2099' = c(2081:2099))
plot_data$ts <-names(time_seg)[1]
for(tt in 1:length((time_seg)))
plot_data$ts[plot_data$yr%in%(time_seg[[tt]][1]:time_seg[[tt]][2])]<-names(time_seg)[tt]
plot_data2 <- plot_data%>%
group_by(var,mo,units,long_name,basin, ts)%>%
summarize(mn_val2 = mean(mn_val))
# now plot phenological shift:
p8 <- ggplot(data = plot_data2 ) +
geom_line( aes(x = mo,y = mn_val2,color=ts),alpha=.8,size=0)+
geom_smooth( aes(x = mo,y = mn_val2,color=ts),
formula = y ~ x, se = F)+
facet_grid(basin~.)+
scale_color_viridis_d(begin=.9,end=.2)+
ylab(tmp_var$units[1])+
ggtitle( paste(aclim[2],mn_NEBS_season$var[1]))+
theme_minimal()
p8
if(update.figs)
ggsave(file=file.path(main,"Figs/PhenShift_large_Zoop.jpg"),width=8,height=5)
September large zooplankton integrated concentration
some text here
Level 2 data can be explored in the same way as the above indices but we will focus in the section below on a simple spatial plot and temporal index. The advantage of Level2 inidces is in the spatial resolution and values outside of the survey area.
# define four time periods
time_seg <- list( '2010-2020' = c(2000:2020),
'2021-2040' = c(2021:2040),
'2041-2060' = c(2041:2060),
'2061-2080' = c(2061:2080),
'2081-2099' = c(2081:2099))
# View an individual variable (e.g., Bottom Temp)
# -------------------------------------------------------
head(srvy_vars)
head(aclim)
# assign the simulation to download
# --> --> Tinker: try selecting a different set of models to compare
sim <-"B10K-H16_CMIP5_MIROC_rcp85"
svl <- list(
'Bottom 5m' = "temp",
'Surface 5m' = "temp",
'Integrated' = c("EupS","Cop","NCaS") )
# Currently available Level 2 variables
dl <- proj_l2_datasets$dataset # datasets
# Let's sample the model years as close to Aug 1 as the model timesteps run:
tr <- c("-08-1 12:00:00 GMT")
# the full grid is large and takes a longtime to plot, so let's subsample the grid every 4 cells
IDin <- "_Aug1_subgrid"
var_use <- "_bottom5m_temp"
# open a "region" or strata specific nc file
fl <- file.path(main,Rdata_path,sim,"Level2",paste0(sim,var_use,IDin,".Rdata"))
# load object 'ACLIMsurveyrep'
if(!file.exists(fl))
get_l2(
ID = IDin,
xi_rangeIN = seq(1,182,10),
eta_rangeIN = seq(1,258,10),
ds_list = dl,
trIN = tr,
sub_varlist = svl,
sim_list = sim )
# load R data file
load(fl) # temp
# there are smarter ways to do this;looping because
# we don't want to mess it up but this is slow...
i <-1
data_long <- data.frame(latitude = as.vector(temp$lat),
longitude = as.vector(temp$lon),
val = as.vector(temp$val[,,i]),
time = temp$time[i],
year = substr( temp$time[i],1,4),stringsAsFactors = F
)
for(i in 2:dim(temp$val)[3])
data_long <- rbind(data_long,
data.frame(latitude = as.vector(temp$lat),
longitude = as.vector(temp$lon),
val = as.vector(temp$val[,,i]),
time = temp$time[i],
year = substr( temp$time[i],1,4),stringsAsFactors = F)
)
# get the mean values for the time blocks from the rdata versions
# will throw "implicit NA" errors that can be ignored
tmp_var <-data_long # get mean var val for each time segment
j<-0
for(i in 1:length(time_seg)){
if(length( which(as.numeric(tmp_var$year)%in%time_seg[[i]] ))>0){
j <- j +1
mn_tmp_var <- tmp_var%>%
filter(year%in%time_seg[[i]],!is.na(val))%>%
group_by(latitude, longitude)%>%
summarise(mnval = mean(val,rm.na=T))
mn_tmp_var$time_period = factor(names(time_seg)[i],levels=names(time_seg))
if(j == 1) mn_var <- mn_tmp_var
if(j > 1) mn_var <- rbind(mn_var,mn_tmp_var)
rm(mn_tmp_var)
}
}
# convert results to a shapefile
L2_sf <- convert2shp(mn_var%>%filter(!is.na(mnval)))
p9 <- plot_stations_basemap(sfIN = L2_sf,
fillIN = "mnval",
colorIN = "mnval",
sizeIN=.6) +
facet_grid(.~time_period)+
scale_color_viridis_c()+
scale_fill_viridis_c()+
guides(
color = guide_legend(title="Bottom T (degC)"),
fill = guide_legend(title="Bottom T (degC)")) +
ggtitle(paste(sim,var_use,IDin))
# This is slow but it works (repeat dev.new() twice if in Rstudio)...
dev.new()
p9
if(update.figs) ggsave(file=file.path(main,"Figs/sub_grid_mn_BT_Aug1.jpg"),width=8,height=6)
# graphics.off()
### 6.2.2 Level 2 projections: Projections at M2 mooring
This study is part of NOAA’s Alaska Climate Integrated Modeling project (ACLIM) and FATE project XXXX. We would like to that the entire ACLIM team including [add specific names] for feedback and discussions on the broader application of this work. Multiple NOAA National Marine Fisheries programs provided support for ACLIM including Fisheries and the Environment (FATE), Stock Assessment Analytical Methods (SAAM) Science and Technology North Pacific Climate Regimes and Ecosystem Productivity, the Integrated Ecosystem Assessment Program (IEA), the NOAA Economics and Social Analysis Division, NOAA Research Transition Acceleration Program (RTAP), the Alaska Fisheries Science Center (ASFC), the Office of Oceanic and Atmospheric Research (OAR) and the National Marine Fisheries Service (NMFS). The scientific views, opinions, and conclusions expressed herein are solely those of the authors and do not represent the views, opinions, or conclusions of NOAA or the Department of Commerce.
Additionally, the International Council for the Exploration of the Sea (ICES) and the North Pacific Marine Science Organization (PICES) provided support for Strategic Initiative for the Study of Climate Impacts on Marine Ecosystems (SI-CCME) workshops, which facilitated development of the ideas presented in this paper. The scientific views, opinions, and conclusions expressed herein are solely those of the authors and do not represent the views, opinions, or conclusions of NOAA, the Department of Commerce, ICES, or PICES.
Meehl, G. A., C. Covey, T. Delworth, M. Latif, B. McAvaney, J. F. B. Mitchell, R. J. Stouffer, and K. E. Taylor, 2007: The WCRP CMIP3 multimodel dataset: A new era in climate change research. Bull. Amer. Meteor. Soc., 88, 1383–1394.
Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012:Anoverview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485–498.
ONeill, B. C., C. Tebaldi, D. P. van Vuuren, V. Eyring, P. Friedlingstein, G. Hurtt, R. Knutti, E. Kriegler, J.-F. Lamarque, J. Lowe, G. A. Meehl, R. Moss, K. Riahi, and B. M. Sanderson. 2016. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geoscientific Model Development 9:3461–3482.
Explore annual indices of downscaled projections for the EBS: ACLIM indices
To view climate change projections from CMIP5 (eventually CMIP6):ESRL climate change portal
This is the hindcast for the Bering Sea and is a combination of the reconstructed climatology from the CLIVAR Co-ordinated Ocean-Ice Reference Experiments (CORE) Climate Model (1969-2006) the NCEP Climate Forecast System Reanalysis is a set of re-forecasts carried out by NOAA’s National Center for Environmental Prediction (NCEP). See CFS-R for more info.
Developed by the Canadian Centre for Climate Modelling and Analysis, this is also known as the CGCM3/T47 model. This model showed the greatest warming over time compared to other models tested by PMEL. See more data the AOOS:CCCMA portal.
The ECHO-G model from the Max Planck Institute in Germany This model showed the least warming over time compared to other models tested by PMEL. See more data the AOOS:ECHO-G portal.
The NOAA Geophysical Fluid Dynamics Laboratory GFDL has lead development of the first Earth System Models (ESMs), which like physical climate models, are based on an atmospheric circulation model coupled with an oceanic circulation model, with representations of land, sea ice and iceberg dynamics; ESMs additionally incorporate interactive biogeochemistry, including the carbon cycle. The ESM2M model used in this project is an evolution of the prototype EMS2.1 model, where pressure-based vertical coordinates are used along the developmental path of GFDL’s Modular Ocean Model version 4.1 and where the land model is more adavanced (LM3) than in the previous ESM2.1
The Model for Interdisciplinary Research on Climate (MIROC)-M model developed by a consortium of agencies in Japan []. Compared to other models tested by PMEL, MIROC-M was intermediate in degree of warming over the Bering Sea shelf for the first half of the 21st century. See more data the AOOS:MIROC portal.